Our contribution is twofold. First, we introduce a benchmark for SSE based on a query-by-example task built on LibriSpeech [1]}. It uses the Mean Average Precision (MAP) metric computed over random query phoneme-ngrams. Previous work have shown that MAP is a good proxy for word segmentation [2]}, [3]}. We show that MAP based on phoneme-ngrams is preferable to a MAP on words, which is too easy and does not reflect the nature of the downstream tasks in unsegmented continuous speech.
